Sparse DataFrame with all-zeros column returns NA for fancy index

[memeplex]

Code Sample, a copy-pastable example if possible

# Your code here

import pandas as pd

df = pd.DataFrame({"x": [0, 0, 0],
                   "y": [4, 5, 6]})
df = df.astype(pd.SparseDtype("float", fill_value=0))
df.iloc[[1]]

Problem description

Executing the above code results in:

        x	y
1	NaN	5.0

Instead of

        x	y
1	0	5.0

This could be related to #27781, although the interpretation of the bug is different.

Expected Output

Mentioned above.

Output of pd.show_versions()

INSTALLED VERSIONS

commit : None
python : 3.7.5.candidate.1
python-bits : 64
OS : Linux
OS-release : 5.3.0-19-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8

pandas : 0.25.3
numpy : 1.17.4
pytz : 2019.3
dateutil : 2.8.1
pip : 19.3.1
setuptools : 41.1.0
Cython : 0.29.14
pytest : None
hypothesis : None
sphinx : 2.2.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.4.1
html5lib : 1.0.1
pymysql : None
psycopg2 : 2.8.4 (dt dec pq3 ext lo64)
jinja2 : 2.10.3
IPython : 7.9.0
pandas_datareader: None
bs4 : 4.8.0
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.4.1
matplotlib : 3.1.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.2
sqlalchemy : 1.3.11
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None

[memeplex]

Here is an even more minimalistic example:

import pandas as pd


s = pd.Series([0])
s = s.astype(pd.SparseDtype("float", fill_value=0))
s[[0]]